Performance Management: The Social Network Analysis Way

As a part of most performance and succession management initiatives taken on by our client, one major challenge is to correctly identify those who arre performing well and those who aren’t. This includes identifying high potentials who, with a little assistance, can be helped into leadership roles and relied upon for directing the company in the future.

Traditional social network analysis method allow for the identifying of those who have a higher proclivity for innovation and collaboration. They tend to be in network positions that we call network brokerage positions or boundary spanners. Not only is innovation implicit in the activities of network brokers, but one study which featured an executive recruiting firm shows that high performing individuals tend to be highly connected individuals inside the organization and at central positions in the network ( See Gandal et al). The conclusion of the study tends to be more unexpected than usual as most recruiting agencies continue to promote external network activities that are designed to increase the size and scope of recruiter’s relationships, rather than incentivize activities that build a more clustered internal network for better information dissemination.

Additionally, researchers found that internal network size and the variable of “in-betweenness” were not only highly correlated to individual revenue generated and the number of contracts fulfilled (placements), but they were more correlated to revenue than human capital and demographic measures. In other words, they described the possibility for individual high performance much better than, say, years of experience or gender. With the standard model of promotion in most organization being that professionals who have more years of experience tend to hold higher organizational positions in the corporate hierarchy, it is clear that certain critical employees can, and often are, incorrectly deemed replaceable in favor of individuals with more experience or who hold higher levels of apparent authority.

Interestingly, the size and connectedness of an individual externally to the organization did not correlate with higher revenues and thereby, higher performance (in this case). This is yet another example of the sometimes mistaken decision to retain highly visible individuals in their industry and has ramification to change management situations beyond mergers and acquisitions. The results also demonstrated the misconception that individuals who are highly connected outside the organization are more valuable to revenue generation and then are top performers.

However, one question remains unanswered: Does correlation mean causation? Are top performing employees’ top performers because of their internal networks or do employees with strong internal network seem to perform well? The researchers in this study believed that they could not answer this question yet, and left that question to future studies, but they believed that it is clear that at a minimum there was a high correlation between the two. For our purposes of affecting change, it is enough. Without a social network analysis that allows us to see the hidden structure of the organization, the possibility for performance management failure retains its high industry levels (estimated to be between 50% and 70%), because knowledge, innovation, and excellence in performance are highly correlated to success and in most instances, are not easily measured with traditional human capital measures or methods.

In the case of the executive recruiting firm, it is easy to correlate revenue generation with network metrics—executive recruiting is a highly task-oriented business and therefore makes it is easy to track revenue generation i.e. performance to relevant metrics. In other areas, such as service oriented industries and their attached professions, measuring network success becomes more obscure.

In most cases, and especially for larger organizations, tracking performance directly to revenue creation is not possible, and so top performers, good collaborators, and key players in the organization would likely seem to be like any other employee if traditional qualitative or statistical methods are used.